QTLEMM: An R Package for QTL Mapping and Hotspot Detection
Journal
R Journal
Journal Volume
18
Journal Issue
1
Start Page
39
End Page
68
ISSN
2073-4859
Date Issued
2026
Author(s)
Chung, Ping-Yuan
Guo, You-Tsz
Ho, Hsiang-An
Lee, Hsin-I
Wu, Po-Ya
農業部農業試驗所
Zeng, Miao-Hui
Kao, Chen-Hung
Abstract
This paper introduces an R package QTLEMM that implements commonly used and popular statistical methods for QTL mapping and QTL hotspot detection. For QTL mapping, QTLEMM offers a suite of functions for simulating data, computing significance thresholds, and estimating QTL parameters using single-QTL or multiple-QTL methods in diverse experimental populations. These methods encompass linear regression, permutation tests, Gaussian stochastic process, normal mixture models, and truncated normal mixture models. Moreover, QTLEMM accommodates both complete genotyping and selective genotyping data, and enables the fitting and comparison of different models in the analysis. For QTL hotspot detection, QTLEMM devises the statistical framework that can handle both the data from genetical genomics experiments and summarized data, mitigate the underestimation of hotspot thresholds, and also identify various types of hotspots at a very low computational cost. QTLEMM provides numerical and graphical results, and can facilitate the discovery of more significant results in the analysis of quantitative traits in biological studies. © (2026), (Technische Universitaet Wien). All Rights Reserved.
Publisher
Technische Universitaet Wien
Type
journal article